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Record W4239342575 · doi:10.2514/1.t5024

Resistor-Network Formulation of Multitemperature Free-Convection Problems

2017· article· en· W4239342575 on OpenAlexafffund
Sepehr Foroushani, John L. Wright, David Naylor

Bibliographic record

VenueJournal of Thermophysics and Heat Transfer · 2017
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsToronto Metropolitan UniversityUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsConvectionNatural convectionMechanicsResistorIsothermal processCombined forced and natural convectionConvection–diffusion equationForced convectionWork (physics)ThermodynamicsMaterials sciencePhysicsVoltage

Abstract

fetched live from OpenAlex

In recent work, the resistor-network formulation of forced-convection problems and a technique (dQdT) for evaluating the paired convective resistances that characterize the network were presented. This technique entails solutions of the energy equation with perturbed boundary conditions. In the present paper, the dQdT technique is extended to free convection. The analytical solution to the classical two-temperature problem of free convection at an isothermal vertical flat plate is used to verify the technique. Then, dQdT is applied to the three-temperature problem of free convection in an asymmetrically heated vertical channel based on numerical solutions of the energy equation. Sample results are presented and known limits are discussed to demonstrate the validity of the results. This paper is part of a series on the resistor-network formulation of convection problems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.206
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2017
Admission routes2
Has abstractyes

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